{"id":"https://openalex.org/W7151307896","doi":"https://doi.org/10.1109/icmla66185.2025.00135","title":"Difficulty-Driven Fine Training for WisdomNet","display_name":"Difficulty-Driven Fine Training for WisdomNet","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151307896","doi":"https://doi.org/10.1109/icmla66185.2025.00135"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00135","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00135","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133122031","display_name":"Ayomide Afolabi","orcid":null},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ayomide Afolabi","raw_affiliation_strings":["Kennesaw State University,School of Data Science and Analytics,Kennesaw,GA,USA"],"affiliations":[{"raw_affiliation_string":"Kennesaw State University,School of Data Science and Analytics,Kennesaw,GA,USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070184068","display_name":"Ramazan Ayg\u00fcn","orcid":"https://orcid.org/0000-0001-7244-7475"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramazan Aygun","raw_affiliation_strings":["Kennesaw State University,Computer Science Department,Kennesaw,GA,USA"],"affiliations":[{"raw_affiliation_string":"Kennesaw State University,Computer Science Department,Kennesaw,GA,USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133090108","display_name":"Truong X. Tran","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Truong X. Tran","raw_affiliation_strings":["The Pennsylvania State University,Penn State Harrisburg,Middletown,PA,USA"],"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University,Penn State Harrisburg,Middletown,PA,USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5133122031"],"corresponding_institution_ids":["https://openalex.org/I172980758"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87276413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"905","last_page":"910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3352999985218048,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3352999985218048,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.11729999631643295,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.05530000105500221,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.6292999982833862},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5519999861717224},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5424000024795532},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5184000134468079},{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.475600004196167},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.45509999990463257},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.42899999022483826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858000159263611},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.6292999982833862},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5424000024795532},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5184000134468079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5074999928474426},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42590001225471497},{"id":"https://openalex.org/C2779952087","wikidata":"https://www.wikidata.org/wiki/Q11831792","display_name":"Ideal point","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C169806903","wikidata":"https://www.wikidata.org/wiki/Q5937752","display_name":"Human error","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28859999775886536},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00135","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00135","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5205419063568115}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2007339694","https://openalex.org/W2022477494","https://openalex.org/W2282821441","https://openalex.org/W2473418344","https://openalex.org/W3041630056","https://openalex.org/W3046849394","https://openalex.org/W3174629909","https://openalex.org/W4246122681","https://openalex.org/W4254751698","https://openalex.org/W4297684981","https://openalex.org/W4310608578","https://openalex.org/W4388127538","https://openalex.org/W4389953823","https://openalex.org/W4403210411","https://openalex.org/W4404034812","https://openalex.org/W4404654652","https://openalex.org/W4415047661"],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1],"models,":[2],"despite":[3],"achieving":[4],"low":[5],"error":[6,33,128],"rate,":[7],"continue":[8],"to":[9,29,52,73,94,133,137,157,160,163,166,169,182],"encounter":[10],"trust":[11],"issues,":[12],"particularly":[13],"in":[14,141,171],"domains":[15],"where":[16],"a":[17,31,53,64,81,126],"single":[18],"incorrect":[19],"prediction":[20],"can":[21],"be":[22],"costly":[23],"or":[24],"disastrous.":[25],"WisdomNet":[26,57,116],"architecture":[27],"facilitates":[28],"achieve":[30],"zero":[32,127],"rate":[34,67,154],"if":[35],"certain":[36,142],"conditions":[37],"are":[38],"met":[39],"by":[40],"rejecting":[41],"data":[42,109],"instances":[43],"it":[44],"is":[45],"uncertain":[46],"about":[47],"and":[48,68,114,177],"delegating":[49],"those":[50],"cases":[51],"human":[54],"expert.":[55],"However,":[56],"still":[58],"faces":[59],"several":[60],"challenges,":[61],"such":[62],"as":[63],"high":[65],"rejection":[66,101,139,153],"determining":[69],"the":[70,100,111,121,152],"appropriate":[71],"point":[72],"stop":[74,95],"fine-training.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79],"propose":[80],"novel":[82],"technique":[83,104],"called":[84],"Difficulty-Driven":[85],"Fine":[86],"Training":[87],"(DDFT),":[88],"which":[89],"not":[90],"only":[91],"determines":[92],"when":[93],"fine-training":[96,115],"but":[97],"also":[98],"minimizes":[99],"rate.":[102,129],"This":[103],"focuses":[105],"on":[106],"excluding":[107],"difficult":[108],"from":[110,155],"validation":[112,123],"set,":[113],"with":[117],"misclassified":[118],"samples":[119],"until":[120],"new":[122],"set":[124],"achieves":[125],"We":[130],"conducted":[131],"experiments":[132],"identify":[134],"factors":[135],"contributing":[136],"increased":[138],"rates":[140],"datasets.":[143],"Our":[144],"experimental":[145],"results":[146],"show":[147],"that":[148],"our":[149],"method":[150],"reduces":[151],"39.90%":[156],"30.53%,":[158],"96.62%":[159],"23.25%,":[161],"89.09%":[162],"9.80%,":[164],"5.24%":[165],"1.65%,":[167],"94.4%":[168],"20.55%":[170],"drybeans(Sira-Dermasons),":[172],"banana":[173],"quality,":[174],"FashionMNIST(Dress-Shirt),":[175],"MNIST(2-7)":[176],"CIFAR10(Cats-Dogs)":[178],"datasets,":[179],"respectively,":[180],"compared":[181],"an":[183],"ideal":[184],"WisdomNet.":[185]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}
